A systematic study on the role of SentiWordNet in opinion mining

Sentiment lexicons(SL)(aka lexical resources)are the repositories of one or several dictionaries that consist of known and precompiled sentiment terms.These lexicons play an important role in performing several different opinion mining tasks.The efficacy of the lexicon-based approaches in performing opinion mining(OM)tasks solely depends on selecting an appropriate opinion lexicon to analyze the text.Therefore,one has to explore the available sentiment lexicons and then select the most suitable resource.Among available resources,SentiWordNet(SWN)is the most widely used lexicon to perform tasks related to opinion mining.In SWN,each synset of WordNet is being assigned the three sentiment numerical scores;positive,negative and objective that are calculated using by a set of classifiers.In this paper,a detailed and comprehensive review of the work related to opinion mining using Senti-WordNet is provided in a very distinctive way.This survey will be useful for the researchers contributing to the field of opinion mining.Following features make our contribution worthwhile and unique among the reviews of similar kind:(i)our review classifies the existing literature with respect to opinion mining tasks and subtasks(ii)it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels(word,sentences,document,aspect,clause,and concept levels)(iii)this state-ofart review covers each article in the following dimensions:the designated task performed,granularity level of the task completed,results obtained,and feature dimensions,and(iv)lastly it concludes the summary of the related articles according to the granularity levels,publishing years,related tasks(or subtasks),and types of classifiers used.In the end,major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.

Deep learning based computer-aided diagnosis for neuroimaging data: focused review and future potential

Automatic image analysis techniques applied to neuroimaging data in general, and magnetic resonance imaging (MRI), and functional MRI (fMRI) in particular, have proven to be effective computer-aided diagnosis (CAD) tools in neuroscience(1-4)Recently, the advancements in machine learning techniques combined with the wide availability of computational power have proven to be efficient in solving previously difficult problems in analyzing neuroimaging data. At the forefront of these advancements is the usage of deep (artificial) neural network architectures that led robust learning based techniques to attack challenging problems such as segmentation and classification in neuroimaging data[5-8].